"... Abstract—Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully performed by applying evolutionary and, in particular, genetic algorithms, and recently, this app ..."

Abstract—Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully performed by applying evolutionary and, in particular, genetic algorithms, and recently, this approach has been extended by using multiobjective evolutionary algorithms, which can consider multiple conflicting objectives, instead of a single one. The hybridization between multiobjective evolutionary algorithms and fuzzy systems is currently known as multiobjective evolutionary fuzzy systems. This paper presents an overview of multiobjective evolutionary fuzzy systems, describing the main contributions on this field and providing a two-level taxonomy of the existing proposals, in order to outline a well-established framework that could help researchers who work on significant further developments. Finally, some considerations of recent trends and potential research directions are presented. Index Terms—Accuracy–interpretability tradeoff, fuzzy association rule mining, fuzzy control, fuzzy rule-based systems (FRBSs), multiobjective evolutionary algorithms (EAs), multiobjective evolutionary fuzzy systems (MOEFSs). I.

...opriate fuzzy parameters have to be chosen on the basis of an experimental study of the control objective. To overcome this difficulty, the application of EAs was proposed for the design of FLCs [92]–=-=[94]-=-. Two problems arise during this process: The first issue concerns how to establish the structure of the controller; second, the numerical values of the controller’s parameters have to be chosen. Many...

by
Frank Hoffmann
- in BICS Seminar, Royal Institue of Technology, 2001

"... This paper presents a new boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is built in an incremental fashion, in that the evolutionary algorithm extracts one fuzzy ..."

This paper presents a new boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is built in an incremental fashion, in that the evolutionary algorithm extracts one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training instances that are classified correctly by the new rule, such that the next iteration of the evolutionary algorithm focuses the search on those fuzzy rules that capture the currently uncovered or misclassified instances. The weight of a fuzzy rule reflects the relative strength the boosting algorithm assigns to the rule class when it aggregates the casted votes. The method is applied to the Wisconsin breast cancer diagnosis data set. 1

"... Abstract. In this paper, we present a methodology for automatically generating online scheduling strategies for a complex scheduling objective with the help of real life workload data. The scheduling problem includes independent parallel jobs and multiple identical machines. The objective is defined ..."

Abstract. In this paper, we present a methodology for automatically generating online scheduling strategies for a complex scheduling objective with the help of real life workload data. The scheduling problem includes independent parallel jobs and multiple identical machines. The objective is defined by the machine provider and considers different priorities of user groups. In order to allow a wide range of objective functions, we use a rule based scheduling strategy. There, a rule system classifies all possible scheduling states and assigns an appropriate scheduling strategy based on the actual state. The rule bases are developed with the help of a Genetic Fuzzy System that uses workload data obtained from real system installations. We evaluate our new scheduling strategies again on real workload data in comparison to a probability based scheduling strategy and the EASY standard scheduling algorithm. To this end, we select an exemplary objective function that prioritizes some user groups over others. 1

"... Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of th ..."

Behavior-based systems form the basis of autonomous control for many robots. In this article, we demonstrate that a single software framework can be used to represent many existing behavior based approaches. The unified behavior framework presented, incorporates the critical ideas and concepts of the existing reactive controllers. Additionally, the modular design of the behavior framework: 1) simplifies development and testing; 2) promotes the reuse of code; 3) supports designs that scale easily into large hierarchies while restricting code complexity; and 4) allows the behavior based system developer the freedom to use the behavior system they feel will function the best. When a hybrid or three layer control architecture includes the unified behavior framework, a common interface is shared by all behaviors, leaving the higher order planning and sequencing elements free to interchange behaviors during execution to achieve high level goals and plans. The framework’s ability to compose structures from independent elements encourages experimentation and reuse while isolating the scope of troubleshooting to the behavior composition. The ability to use elemental components to build and evaluate behavior structures is demonstrated using the Robocode simulation environment. Additionally, the ability of a reactive controller to change its active behavior during execution is shown in a goal seeking robot implementation.

...Q-learning [10]. Or alternative ANN representations such as CTRANN [15,27], GasNets [27], andsConvolutional NN [24]. Other approaches include evolutionary algorithms that evolve fuzzy based behaviors =-=[20]-=-, or augmentedsneural topologies [35].sWhile reactive architectures that are organized as task based decompositions are responsive and able to operate in dynamicsenvironments, they forfeit the ability...

"... This paper provides an overview on the contribution of soft computing to the field of behavior based robotics. It discusses the role of pure fuzzy, neuro-fuzzy and genetic fuzzy rule-based systems for behavior architectures and adaptation. It reviews a number of applications of soft computing te ..."

This paper provides an overview on the contribution of soft computing to the field of behavior based robotics. It discusses the role of pure fuzzy, neuro-fuzzy and genetic fuzzy rule-based systems for behavior architectures and adaptation. It reviews a number of applications of soft computing techniques to autonomous robot navigation and control.

by
Jorge Villagra, David Herrero-pérez
- IEEE Transactions on Control Systems Technology, 2011

"... Abstract—This work addresses the path tracking problem of industrial guidance systems used by automated guided vehicles (AGVs) in load transfer operations. We focus on the control law that permits to AGVs to operate tracking a predefined route with industrial grade of accuracy, repeatability and rel ..."

Abstract—This work addresses the path tracking problem of industrial guidance systems used by automated guided vehicles (AGVs) in load transfer operations. We focus on the control law that permits to AGVs to operate tracking a predefined route with industrial grade of accuracy, repeatability and reliability. One of the main issues of this problem is related to the important weight variation of AGVs when transporting a load, which induces slipping and skidding effects. Besides, localization error of the guidance system should be taken into account because position estimation is typically performed at a low sample rate. Other key point is that control law oscillations can knock down the load, which gives rise to safety and performance problems. Three control techniques—fuzzy, vector pursuit and flatness-based con-trol—are compared in order to evaluate how they can deal with these problems and satisfy the robustness requirements of such an industrial application. Index Terms—Automated guided vehicles (AGVs), flatness con-trol, fuzzy control, nonlinear robust control, vector pursuit. I.

... an expert is often based on a tedious and unreliable trial and error approach, and therefore, different techniques have been used for the automated design and optimization of fuzzy logic controllers =-=[25]-=-. The proposed solution is based on aMamdani-type [26] fuzzy controller, which consists of a collection of fuzzy rules where each one is composed of a set of fuzzy numbers (antecedent) and real parame...

"... Abstract — This paper presents a comparison of three different design concepts for Genetic Fuzzy systems. We apply a Symbiotic Evolution that uses the Michigan approach and two approaches that are based on the Pittsburgh approach: a complete optimization of the problem and a Cooperative Coevolutiona ..."

Abstract — This paper presents a comparison of three different design concepts for Genetic Fuzzy systems. We apply a Symbiotic Evolution that uses the Michigan approach and two approaches that are based on the Pittsburgh approach: a complete optimization of the problem and a Cooperative Coevolutionary algorithm. The three different Genetic Fuzzy systems are applied to a real-world online problem, the generation of scheduling strategies for Massively Parallel Processing systems. The Genetic Fuzzy systems must classify different scheduling states and decide about a corresponding scheduling strategy within each scheduling state. The main challenge arise in the delayed reward given by a critic. Therefore, it is impossible to directly evaluate the assignment of scheduling strategies to scheduling states. In our paper, the three design concepts are evaluated with real workload traces considering result quality, computational effort, convergence behavior, and robustness. I.

...convergence behavior, and robustness. I. INTRODUCTION Fuzzy systems are usually designed by modeling implicit knowledge of an expert within a set of linguistic variables and Fuzzy rules, see Hoffmann =-=[1]-=-. They have been applied successfully to many real-world problems. Especially Genetic Fuzzy systems are well suited to address classification and automatic rule base generation. They provide mechanism...

Granular self-organizing map (grSOM) for structure identification This work presents a useful extension of Kohonen’s Self-Organizing Map (KSOM) for structure identification in linguistic (fuzzy) system modeling applications. More specifically the granular SOM neural model is presented for inducing a distribution of nonparametric fuzzy interval numbers (FINs) from the data. A FIN can represent a local probability distribution function and/or a conventional fuzzy set; moreover a FIN is interpreted as an information granule. Learning is based on a novel metric distance dK(.,.) between FINs. The metric dK(.,.) can be tuned nonlinearly by a mass function m(x), the latter attaches a weight of significance to a real number ‘x ’ in a data dimension. Rigorous analysis is based on mathematical lattice theory. A grSOM can cope with ambiguity by processing linguistic (fuzzy) input data and/or intervals. This work presents a simple grSOM variant, namely greedy grSOM, for classification. A genetic algorithm (GA) introduces tunable nonlinearities during training. Extensive comparisons are shown with related work from the literature. The practical effectiveness of the greedy grSOM is demonstrated comparatively in three benchmark classification problems. Statistical evidence strongly suggests that the proposed techniques improve classification performance. In addition, the greedy grSOM induces descriptive decision-making knowledge (fuzzy rules) from the training data.

...le preserving topology. Note in addition that the performance of fuzzy control systems can be improved genetically by tuning parameterized membership functions as well as inputoutput scaling factors (=-=Hoffmann, 2001-=-). The important difference here is that a GA computes optimally mass functions for tuning a metric distance between nonparametric fuzzy interval numbers (FINs). Additional novelties include an improv...

"... Abstract. A rule selection scheme of evolutionary algorithm is pro-posed to design fuzzy path planner for shooting ability in robot soccer. The fuzzy logic is good for the system that works with ambiguous in-formation. Evolutionary algorithm is employed to deal with difficulty and tediousness in der ..."

Abstract. A rule selection scheme of evolutionary algorithm is pro-posed to design fuzzy path planner for shooting ability in robot soccer. The fuzzy logic is good for the system that works with ambiguous in-formation. Evolutionary algorithm is employed to deal with difficulty and tediousness in deriving fuzzy control rules. Generic evolutionary al-gorithm, however, evaluate and select chromosomes which may include inferior genes, and generate solutions with uncertainty. To ameliorate this problem, we propose a recombinant rule selection method for gene level selection, which grades genes at the same position in the chromosomes and recombine new parent for next generation. The method was evalu-ated with application of designing the fuzzy path planner, where each fuzzy rule was encoded as a gene. Simulation and experimental results showed the effectiveness and the applicability of the proposed method. 1

... this regard, numerous researches have been dedicated to exploring the use of evolutionary algorithms (EAs) to automate the knowledge acquisition base and construct appropriate rules for a given task =-=[8,9,10]-=-. The evolutionary algorithms employed for this purpose use individuals with a single chromosome whose component genes are characterized as rules for the fuzzy control system. During the evolutionary ...

"... Abstract. A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. One of the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose a so-call ..."

Abstract. A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. One of the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose a so-called “approximative ” representation for fuzzy systems, where the antecedent of the rules are determined by a multivariate membership function defined in terms of Voronoi regions. Such representation guarantees the ɛ-completeness property and provides a synergistic relation between the rules. An evolutionary algorithm based on this representation can evolve all the components of the fuzzy system, and due to the properties of the representation, the algorithm (1) can benefit from the use of geometric genetic operators, (2) does not need genetic repair algorithms, (3) guarantees the completeness property and (4) can implement previous knowledge in a simple way by using adaptive a priori rules. The proposed representation is evaluated on an obstacle avoidance problem with a simulated mobile robot. 1

... the context of controlling complex ill defined processes [7]. A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules =-=[4]-=-. However, there is still no systematic way to perform this process. A large number of methods to automate this process and to evaluate and fine tune the obtained fuzzy controllers have been proposed ...